A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery

Zhang, Guosheng; Perrie, William; Li, Xiaofeng; Zhang, Jun A.

doi:10.1109/TGRS.2016.2631663

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A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery

2017
By Zhang, Guosheng ; Perrie, William ; Li, Xiaofeng ; ...
Source: Ieee Transactions on Geoscience and Remote Sensing, 55(3), 1743-1751.

[PDF-2.32 MB]

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Journal Title:

IEEE Transactions on Geoscience and Remote Sensing
Personal Author:

Zhang, Guosheng ; Perrie, William ; Li, Xiaofeng ; Zhang, Jun A.

Zhang, Guosheng ; Perrie, William ; Li, Xiaofeng ; Zhang, Jun A. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies)

OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies) Less -
Description:

Over the last decades, data from spaceborne synthetic aperture radar (SAR) have been used in hurricane research. However, some issues remain. When wind is at hurricane strength, the wind speed retrievals from single-polarization SAR may have errors, because the backscatter signal may experience saturation and become double valued. By comparison, wind direction retrievals from cross-polarization SAR are not possible until now. In this paper, we develop a 2-D model, the symmetric hurricane estimates for wind (SHEW) model, and combine it with the modified inflow angle model to detect hurricane morphology and estimate the wind vector field imaged by cross-polarization SAR. By fitting SHEW to the SAR derived hurricane wind speed, we find the initial closest ellipticalsymmetrical wind speed fields, hurricane center location, major and minor axes, the azimuthal (orientation) angle relative to the reference ellipse, and maximum wind speed. This set of hurricane morphology parameters, along with the speed of hurricane motion, are input to the inflow angle model, modified with an ellipse-shaped eye, to derive the hurricane wind direction. A total of 14 RADARSAT-2 ScanSAR images are employed to tune the combined model. Two SAR images acquired over Hurricane Arthur (2014) and Hurricane Earl (2010) are used to validate this model. Comparisons between the modeled surface wind vector and measurements from airborne stepped-frequency microwave radiometer and dropwindsondes show excellent agreement. The proposed method works well in areas with significant radar attenuation by precipitation.
Source:

Ieee Transactions on Geoscience and Remote Sensing, 55(3), 1743-1751.
DOI:

https://doi.org/10.1109/TGRS.2016.2631663
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Journal Article
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urn:sha256:3a422a925a843c6f12b8acfc1c21f33a7b2a8c69d2a3c54dec89076e92aa38de
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https://repository.library.noaa.gov/view/noaa/16156/noaa_16156_DS1.pdf
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A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery

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